How to Use Helicone in Your n8n Workflows

Juliette Chevalier· October 24, 2025

How to Use Helicone in Your n8n Workflows

Want to monitor your LLM calls in n8n, while taking advantage of Helicone's AI Gateway? Here's how to set up Helicone's community node in under 5 minutes.

Table of Contents

• What You're Building
• Prerequisites
• Step 1: Self-Host n8n with Docker
• Step 2: Install the Helicone Community Node
• Step 3: Configure Helicone Credentials
• Step 4: Build Your First Workflow
• Step 5: Verify in Helicone Dashboard
• Optional: Add Extra Observability Features
• What's Next?
• Troubleshooting

What You're Building

By the end of this tutorial, you'll have:

• A self-hosted n8n instance running locally
• The Helicone n8n node installed
• A working workflow that logs all LLM interactions to your Helicone dashboard

Prerequisites

• Docker installed on your machine
• A Helicone API key (get one here)

Step 1: Self-Host n8n with Docker

The fastest way to get n8n running is with Docker. This gives you a public URL to work with webhooks and integrations.

Make sure you have Docker installed and running on your machine.

docker --version

If you don't have Docker installed, you can install it here.

Then, create a new directory for your n8n instance and run the following command to start the container:

mkdir self-hosted-n8n
cd self-hosted-n8n
docker volume create n8n_data

docker run -it --rm \
  --name n8n \
  -p 5678:5678 \
  -e GENERIC_TIMEZONE="America/New_York" \
  -e TZ="America/New_York" \
  -e N8N_ENFORCE_SETTINGS_FILE_PERMISSIONS=true \
  -e N8N_RUNNERS_ENABLED=true \
  -v n8n_data:/home/node/.n8n \
  docker.n8n.io/n8nio/n8n

Open http://localhost:5678 and sign up for your new account.

Step 2: Install the Helicone Community Node

From the n8n interface:

1. Click the user menu (bottom left corner)
2. Select Settings
3. Go to Community Nodes (last on the left sidebar)
4. Click Install a community node
5. Enter the package name: n8n-nodes-helicone and click on the checkbox below it
6. Click Install

Wait ~30 seconds for the installation to complete. The node will appear automatically in your nodes panel.

Step 3: Configure Helicone Credentials

Before using the node, add your Helicone API key:

1. Click back on Settings and select the Credentials tab

2. Click Add Credential
3. Search for "Helicone" and select Helicone LLM Observability

4. Get your API key from your Helicone dashboard and enter it in the API Key field
5. Click Save

Step 4: Build Your First Workflow

Now let's create a simple workflow that asks an LLM a question and logs it to Helicone.

1. Click Create Workflow
2. Select the "+" icon and search for "Helicone" in the nodes panel (you should see the Helicone Chat Model node) and click "Add to workflow"

3. On the bottom left Chat Input panel, ask a question: "What is the capital of Panama?"
4. See the response appear in the output panel with your input variables populated on the right side panel

Step 5: Verify in Helicone Dashboard

Open your Helicone dashboard and head over to the "Requests" tab. You'll see:

• The request logged with model gpt-4o-mini
• Token usage and costs
• Response time metrics
• The full prompt and completion

Congratulations! You have now created your first workflow that logs to Helicone. Make sure to set it as "Active" in your dashboard.

Optional: Add Extra Observability Features

The Helicone n8n node already comes with observability included, but you can add extra features to your workflows to get more insights on your Helicone dashboard. For example:

Custom Properties

Tag requests for filtering and analysis:

{
  "environment": "production",
  "user_id": "user_123",
  "feature": "chat"
}

Session Tracking

Group related requests together:

• Session ID: chat_session_456
• Session Name: Customer Support Chat
• Session Path: support/chat

Response Caching

Enable caching to reduce costs and improve latency:

• Enable Caching: true
• Cache TTL: 3600 (1 hour in seconds)

What's Next?

Now that you have Helicone monitoring set up in n8n:

1. LLM workflows - Chain multiple LLM calls and see the full trace
2. Monitor costs - Track spending across different models and use cases
3. Debug faster - Inspect failed requests with full context
4. Optimize performance - Identify slow requests and cache common patterns

Troubleshooting

Model not found? Check helicone.ai/models for the exact model name. Different providers use different naming conventions.

Need help? Join our Discord community where engineers share tips and troubleshoot together.

Want to dive deeper? Check out the Helicone n8n node on GitHub or explore the full Helicone documentation.